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Volumn 33, Issue 12, 2012, Pages 1638-1646

A reduct derived from feature selection

Author keywords

Attribute reduction; Data mining; Decision systems; Feature selection; Rough sets

Indexed keywords

ATTRIBUTE REDUCTION; DATA SETS; DECISION INFORMATION SYSTEM; DECISION SYSTEMS; OPTIMAL ALGORITHM; OPTIMAL SOLUTIONS; ROUGH SET; UCI REPOSITORY;

EID: 84862338473     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2012.03.028     Document Type: Article
Times cited : (11)

References (33)
  • 1
    • 9644265275 scopus 로고    scopus 로고
    • A feature selection technique for classificatory analysis
    • A. Ahmad, and L. Dey A feature selection technique for classificatory analysis Pattern Recognition Lett. 26 1 2005 43 56
    • (2005) Pattern Recognition Lett. , vol.26 , Issue.1 , pp. 43-56
    • Ahmad, A.1    Dey, L.2
  • 2
    • 0004493166 scopus 로고    scopus 로고
    • On the approximation of minimizing non zero variables or unsatisfied relations in linear systems
    • E. Amaldi, and V. Kann On the approximation of minimizing non zero variables or unsatisfied relations in linear systems Theor. Comput. Sci. 209 1-2 1998 237 260
    • (1998) Theor. Comput. Sci. , vol.209 , Issue.12 , pp. 237-260
    • Amaldi, E.1    Kann, V.2
  • 3
    • 0032856761 scopus 로고    scopus 로고
    • On domain knowledge and feature selection using a support vector machine
    • O. Barzilay, and V.L. Brailovsky On domain knowledge and feature selection using a support vector machine Pattern Recognition Lett. 20 5 1999 475 484
    • (1999) Pattern Recognition Lett. , vol.20 , Issue.5 , pp. 475-484
    • Barzilay, O.1    Brailovsky, V.L.2
  • 4
    • 0242302657 scopus 로고    scopus 로고
    • Consistency-based search in feature selection
    • M. Dash, and H. Liu Consistency-based search in feature selection Artif. Intell. 151 1-2 2003 155 176
    • (2003) Artif. Intell. , vol.151 , Issue.12 , pp. 155-176
    • Dash, M.1    Liu, H.2
  • 5
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demsar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 1-30
    • Demsar, J.1
  • 6
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • T. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Comput. 10 1998 1895 1924
    • (1998) Neural Comput. , vol.10 , pp. 1895-1924
    • Dietterich, T.1
  • 7
    • 78951477592 scopus 로고    scopus 로고
    • Improving dynamic facial expression recognition with feature subset selection
    • F. Dornaika, E. Lazkano, and B. Sierra Improving dynamic facial expression recognition with feature subset selection Pattern Recognition Lett. 32 5 2011 740 748
    • (2011) Pattern Recognition Lett. , vol.32 , Issue.5 , pp. 740-748
    • Dornaika, F.1    Lazkano, E.2    Sierra, B.3
  • 8
    • 33745355536 scopus 로고    scopus 로고
    • The five-factor model of the positive and negative syndrome scale II: A ten-fold cross-validation of a revised model
    • M. Gaag, T. Hoffman, and M. Remijsen The five-factor model of the positive and negative syndrome scale II: A ten-fold cross-validation of a revised model Schizophr. Res. 85 1-3 2006 280 287
    • (2006) Schizophr. Res. , vol.85 , Issue.13 , pp. 280-287
    • Gaag, M.1    Hoffman, T.2    Remijsen, M.3
  • 9
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach. Learn. 46 2002 389 422
    • (2002) Mach. Learn. , vol.46 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 10
    • 85065703189 scopus 로고    scopus 로고
    • Correlation-based feature selection for discrete and numeric class machine learning
    • Hall, M. 2000. Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of 17th International Conference Machine Learning, pp. 359-366.
    • (2000) Proceedings of 17th International Conference Machine Learning , pp. 359-366
    • Hall, M.1
  • 11
    • 46749140199 scopus 로고    scopus 로고
    • Neighborhood rough set based heterogeneous feature subset selection
    • Q. Hu, D. Yu, J. Liu, and C. Wu Neighborhood rough set based heterogeneous feature subset selection Inform. Sci. 178 2007 3577 3594
    • (2007) Inform. Sci. , vol.178 , pp. 3577-3594
    • Hu, Q.1    Yu, D.2    Liu, J.3    Wu, C.4
  • 12
    • 32644440353 scopus 로고    scopus 로고
    • Information-preserving hybrid data reduction based on fuzzy-rough techniques
    • Q. Hu, D. Yu, and Z. Xie Information-preserving hybrid data reduction based on fuzzy-rough techniques Pattern Recognition Lett. 27 2006 414 423
    • (2006) Pattern Recognition Lett. , vol.27 , pp. 414-423
    • Hu, Q.1    Yu, D.2    Xie, Z.3
  • 13
    • 62349118015 scopus 로고    scopus 로고
    • Feature selection with dynamic mutual information
    • H. Liu, J. Sun, L. Liu, and H. Zhang Feature selection with dynamic mutual information Pattern Recognit. 2009 1330 1339
    • (2009) Pattern Recognit. , pp. 1330-1339
    • Liu, H.1    Sun, J.2    Liu, L.3    Zhang, H.4
  • 18
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
    • H. Peng, F. Long, and C. Ding Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Machine Intell. 27 8 2005 1226 1238
    • (2005) IEEE Trans. Pattern Anal. Machine Intell. , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3
  • 19
    • 38248999905 scopus 로고
    • An analysis of the max-min approach to feature selection and ordering
    • P. Pudil, J.N.N. Choakjarernwanit, and J. Kittler An analysis of the max-min approach to feature selection and ordering Pattern Recognition Lett. 14 11 1993 841 847
    • (1993) Pattern Recognition Lett. , vol.14 , Issue.11 , pp. 841-847
    • Pudil, P.1    Choakjarernwanit, J.N.N.2    Kittler, J.3
  • 20
    • 77955560086 scopus 로고    scopus 로고
    • A learning approach to hierarchical feature selection and aggregation for audio classification
    • P. Ruvolo, I. Fasel, and J.R. Movellan A learning approach to hierarchical feature selection and aggregation for audio classification Pattern Recognition Lett. 31 12 2010 1535 1542
    • (2010) Pattern Recognition Lett. , vol.31 , Issue.12 , pp. 1535-1542
    • Ruvolo, P.1    Fasel, I.2    Movellan, J.R.3
  • 21
    • 0032708886 scopus 로고    scopus 로고
    • Feature selection for multiple binary classification problems
    • Y. Shapira, and I. Gath Feature selection for multiple binary classification problems Pattern Recognition Lett. 20 8 1999 823 832
    • (1999) Pattern Recognition Lett. , vol.20 , Issue.8 , pp. 823-832
    • Shapira, Y.1    Gath, I.2
  • 22
    • 0141990695 scopus 로고    scopus 로고
    • Theoretical and empirical analysis of relieff and rrelieff
    • M. Sikonja, and I. Kononenko Theoretical and empirical analysis of relieff and rrelieff Mach. Learn. 2003 53
    • (2003) Mach. Learn. , pp. 53
    • Sikonja, M.1    Kononenko, I.2
  • 23
    • 0002395767 scopus 로고
    • The discernibility matrices and functions in information systems
    • R. Slowinski, Handbook of Applications and Advances of the Rough Sets Theory Kluwer Dordrecht
    • A. Skowron, and C. Rauszer The discernibility matrices and functions in information systems R. Slowinski, Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory 1992 Kluwer Dordrecht
    • (1992) Intelligent Decision Support
    • Skowron, A.1    Rauszer, C.2
  • 24
    • 0036948613 scopus 로고    scopus 로고
    • Approximate entropy reducts
    • D. Slézak Approximate entropy reducts Fund. Inform. 53 2002 365 390
    • (2002) Fund. Inform. , vol.53 , pp. 365-390
    • Slézak, D.1
  • 25
    • 0037332841 scopus 로고    scopus 로고
    • Rough set methods in feature selection and recognition
    • R.W. Swiniarski, and A. Skowron Rough set methods in feature selection and recognition Pattern Recognition Lett. 24 6 2003 833 849
    • (2003) Pattern Recognition Lett. , vol.24 , Issue.6 , pp. 833-849
    • Swiniarski, R.W.1    Skowron, A.2
  • 26
    • 77953138030 scopus 로고    scopus 로고
    • Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection
    • M.A. Tahir, and J. Smith Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection Pattern Recognition Lett. 31 11 2010 1470 1480
    • (2010) Pattern Recognition Lett. , vol.31 , Issue.11 , pp. 1470-1480
    • Tahir, M.A.1    Smith, J.2
  • 27
    • 0001127246 scopus 로고
    • On optimal decision rules in decision tables
    • S. Wong, and W. Ziarko On optimal decision rules in decision tables Bull. Polish Acad. Sci. 33 1985 693 696
    • (1985) Bull. Polish Acad. Sci. , vol.33 , pp. 693-696
    • Wong, S.1    Ziarko, W.2
  • 29
    • 58249098798 scopus 로고    scopus 로고
    • Discernibility matrix simplification for constructing attribute reducts
    • Y. Yao, and Y. Zhao Discernibility matrix simplification for constructing attribute reducts Inform. Sci. 179 2009 867 882
    • (2009) Inform. Sci. , vol.179 , pp. 867-882
    • Yao, Y.1    Zhao, Y.2
  • 30
    • 0022194379 scopus 로고
    • Optimal linear feature selection for a general class of statistical pattern recognition models
    • D.M. Young, P.L. Odell, and V.R. Marco Optimal linear feature selection for a general class of statistical pattern recognition models Pattern Recognition Lett. 3 3 1985 161 165
    • (1985) Pattern Recognition Lett. , vol.3 , Issue.3 , pp. 161-165
    • Young, D.M.1    Odell, P.L.2    Marco, V.R.3
  • 31
    • 0036132565 scopus 로고    scopus 로고
    • Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery
    • S. Yu, S.D. Backer, and P. Scheunders Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery Pattern Recognition Lett. 23 1-3 2002 183 190
    • (2002) Pattern Recognition Lett. , vol.23 , Issue.13 , pp. 183-190
    • Yu, S.1    Backer, S.D.2    Scheunders, P.3
  • 32
    • 25144492516 scopus 로고    scopus 로고
    • Efficient feature selection via analysis of relevance and redundancy
    • L. Yu, and H. Liu Efficient feature selection via analysis of relevance and redundancy J. Mach. Learn. Res. 5 2004 1205 1224
    • (2004) J. Mach. Learn. Res. , vol.5 , pp. 1205-1224
    • Yu, L.1    Liu, H.2
  • 33
    • 60249087028 scopus 로고    scopus 로고
    • Different metaheuristic strategies to solve the feature selection problem
    • S.C. Yusta Different metaheuristic strategies to solve the feature selection problem Pattern Recognition Lett. 30 5 2009 525 534
    • (2009) Pattern Recognition Lett. , vol.30 , Issue.5 , pp. 525-534
    • Yusta, S.C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.